Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I'm Charan Lingala, an AI Software Engineer with over 4 years of experience building and deploying AI-powered applications using Python, LLMs, and cloud platforms. I enjoy turning complex data into practical AI solutions. I'm passionate about Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, and backend API development. I have hands-on experience integrating LLMs with enterprise data, designing data pipelines, and building scalable AI systems using FastAPI, LangChain, and vector databases. I enjoy creating intelligent applications like chatbots and document intelligence systems and I continually improve model performance through iteration, monitoring, and experimentation.…I'm Charan Lingala, an AI Software Engineer with over 4 years of experience building and deploying AI-powered applications using Python, LLMs, and cloud platforms. I enjoy turning complex data into practical AI solutions. I'm passionate about Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, and backend API development. I have hands-on experience integrating LLMs with enterprise data, designing data pipelines, and building scalable AI systems using FastAPI, LangChain, and vector databases. I enjoy creating intelligent applications like chatbots and document intelligence systems and I continually improve model performance through iteration, monitoring, and experimentation.

Charan Lingala

AI Engineer, Data Scientist, Full Stack Developer, +2





I'm Charan Lingala, an AI Software Engineer with over 4 years of experience building and deploying AI-powered applications using Python, LLMs, and cloud platforms. I enjoy turning complex data into practical AI solutions. I'm passionate about Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, and backend API development. I have hands-on experience integrating LLMs with enterprise data, designing data pipelines, and building scalable AI systems using FastAPI, LangChain, and vector databases. I enjoy creating intelligent applications like chatbots and document intelligence systems and I continually improve model performance through iteration, monitoring, and experimentation.…I'm Charan Lingala, an AI Software Engineer with over 4 years of experience building and deploying AI-powered applications using Python, LLMs, and cloud platforms. I enjoy turning complex data into practical AI solutions. I'm passionate about Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, and backend API development. I have hands-on experience integrating LLMs with enterprise data, designing data pipelines, and building scalable AI systems using FastAPI, LangChain, and vector databases. I enjoy creating intelligent applications like chatbots and document intelligence systems and I continually improve model performance through iteration, monitoring, and experimentation.

Available to hire

I’m Charan Lingala, an AI Software Engineer with over 4 years of experience building and deploying AI-powered applications using Python, LLMs, and cloud platforms. I enjoy turning complex data into practical AI solutions.

I’m passionate about Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, and backend API development. I have hands-on experience integrating LLMs with enterprise data, designing data pipelines, and building scalable AI systems using FastAPI, LangChain, and vector databases. I enjoy creating intelligent applications like chatbots and document intelligence systems and I continually improve model performance through iteration, monitoring, and experimentation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Software Engineer - AI/ML at JP Morgan Chase & Co.

August 1, 2025 - Present

Designed and deployed RAG pipelines using LangChain and FAISS integrated with Azure OpenAI, enabling intelligent search over enterprise financial documents and reducing manual query resolution by 35%. Built and maintained FastAPI-based REST APIs serving AI inference endpoints for document intelligence and NLP services, maintaining response latency under 200ms across high-traffic financial applications. Implemented data ingestion pipelines using Azure Data Factory and Delta Lake to feed downstream machine learning model workflows. Applied few-shot and chain-of-thought prompt engineering techniques to optimize LLM outputs for financial QA tasks, improving response relevance and accuracy by ~40%. Integrated Azure AI Search with internal knowledge bases to power conversational assistants, enabling natural language retrieval of compliance documents and policy records for enterprise users. Containerized AI microservices using Docker and integrated into CI/CD pipelines via GitHub Actions. Imp

Software Engineer - Data&ML – Customer Analytics at LTIMindtree

October 1, 2022 - July 1, 2023

Built an end-to-end customer churn prediction pipeline using XGBoost and Scikit-learn on telecom behavioral and billing data across 500K+ customer records, achieving model accuracy above 85%. Developed FastAPI and Flask-based REST API endpoints to expose trained ML models for real-time churn scoring, enabling integration with internal CRM and analytics dashboards. Engineered feature extraction workflows using Pandas and NumPy, transforming raw telecom data including usage patterns, billing cycles, and customer tenure into clean, model-ready features. Deployed and managed ML models on AWS SageMaker, configuring scalable training jobs and inference endpoints to support reliable, cloud-native model serving in production environments. Automated ETL pipelines using Python and SQL, reducing manual data preparation effort by 40% and ensuring consistent, validated data delivery to training workflows daily. Applied SHAP-based explainability analysis to identify top churn drivers across customer

Junior Software Engineer – Risk & Fraud Analytics at HSBC

July 1, 2020 - September 1, 2022

Developed Python-based backend data processing systems to ingest, validate, and transform large-scale financial transaction records, supporting downstream fraud detection and risk scoring workflows. Built and integrated XGBoost-based fraud detection models into production via REST APIs, enabling real-time transaction risk scoring across millions of daily banking operations. Engineered anomaly detection pipelines using statistical feature engineering and behavioral pattern analysis, improving fraud detection accuracy by 20% and reducing false positive rates by 15%. Designed and implemented batch and real-time data pipelines using Python and SQL, reducing end-to-end processing latency by 25% and improving data reliability across high-volume transaction flows. Optimized complex SQL queries and database operations for large transactional datasets, reducing query execution time by 30% and improving retrieval throughput for risk analytics teams. Created modular Python scripts for data valida